Abstract: Maintaining optimal water quality is crucial for successful aquaculture. This necessitates careful management of various water quality parameters, including pH levels within their ideal range. There is growing interest in creating affordable optical pH sensors that provide accurate readings across a wide range of pH values. Development of sensors that are both accurate and cost-effective remains a challenge. To this end, this study demonstrates the use of machine learning with mango leaf extract as a colorimetric indicator to achieve accurate and cost-effective pH estimation for aquaculture practices. Mango leaf was utilized as the pH indicator, covering a range from 1 to 13. RGB color extraction and Exif data were used for image analysis to extract relevant features. The XGBoost algorithm, optimized through stepwise hyperparameter tuning with early stopping, was used to train three different models on this dataset to predict pH values. Three classification models, namely Y3, Y5, and Y13, were trained with 3, 5, and 13 output classes, respectively. The overall precision achieved by each model was 0.94, 0.85, and 0.72, respectively. This demonstrates the potential of this approach for developing a user-friendly yet cost-effective sensor for pH detection applicable in aquaculture practices. The proposed method could help aquaculture farmers an affordable and intelligent smartphone-based pH detection tool, enhancing water quality management while reducing the need for expensive instruments and eliminating the need for additional costly and time-consuming experimental work, thereby contributing to the sustainability of aquaculture practices.

Hajar Rastegari, Romi Fadilah Rahmat and Farhad Nadi, “A Machine Learning Approach to pH Monitoring: Mango Leaf Colorimetry in Aquaculture” International Journal of Advanced Computer Science and Applications(IJACSA), 15(11), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01511130

@article{Rastegari2024,
title = {A Machine Learning Approach to pH Monitoring: Mango Leaf Colorimetry in Aquaculture},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01511130},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01511130},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {11},
author = {Hajar Rastegari and Romi Fadilah Rahmat and Farhad Nadi}
}